title: Statistical methods and models for bridging Omics data levels.
abstract: Multiple Omics datasets (for example, high throughput mRNA and protein measurements for the same set of genes) are beginning to appear more widely within the fields of bioinformatics and computational biology. There are many tools available for the analysis of single datasets but two (or more) sets of coupled observations present more of a challenge. I describe some of the methods available - from classical statistical techniques to more recent advances from the fields of Machine Learning and Pattern Recognition for linking Omics data levels with particular focus on transcriptomics and proteomics profiles.
authors.affiliation: Inference Research Group, Department of Computing Science, University of Glasgow, Glasgow, UK. srogers@dcs.gla.ac.uk
authors.forename: Simon
authors.initials: S
authors.lastname: Rogers
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D019451:Information Management / Q000379:methods*; D015233:Models, Statistical; D015999:Multivariate Analysis; D040901:Proteomics